The sedimentation characteristics of low-rank coal slurry with saline wastewater as a coagulant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The hard-to-treated saline wastewater from the coal chemical industry was investigated as a coagulant in coal slurry sedimentation. In this paper, the effects of saline wastewater as a coagulant on sedimentation characteristics of low-rank coal slurry were studied. Based on the multiple-light-scattering technique, the backscattering (BS), turbiscan stability index (TSI), and supernatant thickness (ST) of the coal slurry system were analyzed at different saline dosages. The ion concentrations in coal slurry were compared with and without saline wastewater. The results showed that saline wastewater was an effective coagulant for coal slurry. Without saline wastewater, the static stability of coal slurry was stable and the BS value almost constant upon the setting time. However, the BS value increased sharply when saline wastewater was added, especially at the upper layer of coal slurry, indicating the coagulation of coal fine particles. With the increase of saline dosage, the floc size increased, resulting in a decrease in the static stability of coal slurry. The TSI value reached the maximum when saline dosage was 50.0 kg/t. Furthermore, the addition of saline wastewater can further enhance the settling rate of coal particles when anionic polyacrylamide was used as a flocculant. The fastest settling rate and minimum turbidity were obtained at the dosages of 12.5 g/t polyacrylamide and 50 kg/t saline. The beneficial effects of saline wastewater in coal slurry coagulation were studied by zeta potential analysis, which indicates that the addition of saline wastewater contributes to depress the electric double layer of coal particles in aqueous suspension, resulting in lower negative surface charges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it